We present a simple but powerful reinterpretation of kernelized locality-sensitive hashing (KLSH), a general and popular method developed in the vision community for performing approximate nearest-neighbor searches in an arbitrary reproducing kernel Hilbert space (RKHS). Our new perspective is based on viewing the steps of the KLSH algorithm in an appropriately projected space, and has several key theoretical and practical benefits. First, it eliminates the problematic conceptual difficulties that are present in the existing motivation of KLSH. Second, it yields the first formal retrieval performance bounds for KLSH. Third, our analysis reveals two techniques for boosting the empirical performance of KLSH. We evaluate these extensions on several large-scale benchmark image retrieval data sets, and show that our analysis leads to improved recall performance of at least 12%, and sometimes much higher, over the standard KLSH method.

本文提出了一种针对核化局部敏感哈希（KLSH）的简单而强大的重新解释方法，该方法是在投影空间中查看KLSH算法步骤的基础上实现的，并具有几个关键的理论和实际优点，包括消除KLSH的现有动机中存在的问题，获取KLSH的首个正式检索性能界限，利用改进的技术提高KLSH的经验性能，并在几个大规模基准图像检索数据集上进行了评估，表明该分析可至少提高12%的召回率性能，并且有时比标准KLSH方法高得多。

基于核化局部敏感哈希的大规模图像检索改进研究